With the accelerating pace of digital transformation and the widespread adoption of online platforms, both social and technical concerns regarding dark patterns-user interface designs that undermine users’ability to make informed and rational choices-have become increasingly prominent. As corporate online platforms grow more sophisticated in their design strategies, there is a pressing need for proactive and real-time detection technologies that go beyond the predominantly reactive approaches employed by regulatory authorities. In this paper, we propose a visual dark pattern detection framework that improves both detection accuracy and real-time performance.
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Building UI/UX Dataset for Dark Pattern Detection and YOLOv12x-based Real-Time Object Recognition Detection System
A visual dark pattern detection framework that improves both detection accuracy and real-time performance is proposed that achieves a high detection accuracy and real-time inference speed, confirming its effectiveness for practical deployment in online environments.